Analysis of the Results of Heel-Rise Test with Sensors: A Systematic Review

A Method of Precise Auto-Calibration in a Micro-Electro-Mechanical System Accelerometer

A novel design of a MEMS (Micro-Electromechanical System) capacitive accelerometer fabricated by surface micromachining, with a structure enabling precise auto-calibration during operation, is presented. Precise auto-calibration was introduced to ensure more accurate acceleration measurements compared to standard designs. The standard mechanical structure of the accelerometer (seismic mass integrated with elastic suspension and movable plates coupled with fixed plates forming a system of differential sensing capacitors) was equipped with three movable detection electrodes coupled with three fixed electrodes, thus creating three atypical tunneling displacement transducers detecting three specific positions of seismic mass with high precision, enabling the auto-calibration of the accelerometer while it was being operated. Auto-calibration is carried out by recording the accelerometer indication while the seismic mass occupies a specific position, which corresponds to a known value of acting acceleration determined in a pre-calibration process. The diagram and the design of the mechanical structure of the accelerometer, the block diagram of the electronic circuits, and the mathematical relationships used for auto-calibration are presented. The results of the simulation studies related to mechanical and electric phenomena are discussed.

Fatigue of the intrinsic foot core muscles had a greater effect on gait than extrinsic foot core muscles: A time-series based analyze

BACKGROUND

The Heel Rise endurance (HRE) which indicates the extrinsic foot core (ECO) muscle's performance and the paper grip endurance (PGE) which indicates the intrinsic foot core (ICO) muscle's performance are essential components of a healthy foot function. However, the foot core muscles' fatigue response on spatial and temporal gait parameters after the HRE and the PGE tests were not adequately investigated. The purpose of this study was to determine whether the fatigue of the ICO and the ECO muscles affect gait parameters.

MATERIAL AND METHODS

A prospective, cross-sectional study was conducted on 22 sedentary individuals (44 feet). Gait was investigated pre and after the Heel Rise (HR) endurance test and the paper grip (PG) endurance test by inertial sensors. At least 500 consecutive steps were collected for each individual. Spatial-temporal gait parameters were used as outcome measures.

RESULTS

ECO fatigue and ICO fatigue led to increases in the step length (p < 0.05) and the stride lengths (p < 0.05), the single support (p < 0.05), and the terminal stance durations (p < 0.05). It was also seen that ICO fatigue had a greater effect on gait than ECO fatigue. The ECO fatigue had a medium to large effect on the gait parameters (d=0.313-0.646). The ICO fatigue affected gait with a large effect (d=0.524-2.048).

CONCLUSION

The ECO fatigue and the ICO fatigue led to clinically important changes in long-range gait parameters and the ICO fatigue had a greater effect on gait than ECO fatigue. It was suggested that clinicians add ICO muscle endurance training to improve the physical performance of individuals.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

A smart insole system capable of identifying proper heel raise posture for chronic ankle instability rehabilitation

Heel raise is widely prescribed to patients with chronic ankle instability in order to strengthen the Peroneus Longus muscle (PL) which supports the weakened lateral collateral ligaments. While the exercise itself is intuitive, ankle orientation is of particular importance because heel raises performed with inversion do not well recruit the PL. This implies that proper execution is imperative and a means to assess heel raise training sessions is needed. In this study we present a smart insole system capable of identifying heel raise events and its corresponding rise, hold and drop phases, which allows for a more descriptive analysis. The results from our heel raise sessions, which consist of four different variants performed by five healthy subjects, suggest that medial–lateral foot pressure distribution and foot orientation are needed to differentiate heel raises performed with ankle eversion and inversion. We go further and substantiate that proper execution, detected by our system, indeed leads to increased PL activation by analyzing the electromyography signals. We believe that the proposed system may provide clinicians with invaluable information regarding onsite as well as at-home training and possibly, with biofeedback, serve as foundation for software as a medical device.

Can the Eight Hop Test Be Measured with Sensors? A Systematic Review

Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors.

Electronic Solutions for Artificial Intelligence Healthcare

At present, diverse, innovative technology is used in electronics and ubiquitous computing environments [...]

A Brief Review on the Sensor Measurement Solutions for the Ten-Meter Walk Test

The wide-spread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative solutions for physiotherapy striving for accurate and early identification of various pathologies. An essential and reliable performance measure is the ten-meter walk test, which is employed to determine functional mobility, gait, and vestibular function. Sensor-based approaches can identify the various test phases and their segmented duration, among other parameters. The measurement parameter primarily used is related to the tests’ duration, and after identifying patterns, a variety of physical treatments can be recommended. This paper reviews multiple studies focusing on automated measurements of the ten-meter walk test with different sensors. Most of the analyzed studies measure similar parameters as traditional methods, such as velocity, duration, and other involuntary and dangerous patients’ movements after stroke. That provides an opportunity to measure different parameters that can be later fed into machine learning models for analyzing more complex patterns.

Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.